Overview

Dataset statistics

Number of variables5
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory43.3 B

Variable types

Categorical4
Text1

Dataset

Description서울특별시 양천구 모래주머니 배치현황( 행정동, 위치, 배치수량, 데이터기준일자 등)으로 수방 및 제설 소모품 구비 현황과 관련한 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15105023/fileData.do

Alerts

배치수량 has constant value ""Constant
데이터기준일자 has constant value ""Constant
소재지 주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:38:20.319584
Analysis finished2023-12-12 09:38:20.616403
Duration0.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct2
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size588.0 B
전진배치
39 
주민센터
18 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전진배치
2nd row전진배치
3rd row전진배치
4th row전진배치
5th row전진배치

Common Values

ValueCountFrequency (%)
전진배치 39
68.4%
주민센터 18
31.6%

Length

2023-12-12T18:38:20.681983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:38:20.799211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전진배치 39
68.4%
주민센터 18
31.6%

행정동
Categorical

Distinct18
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Memory size588.0 B
신정4동
8 
신월3동
6 
신월1동
6 
목4동
5 
신월4동
3 
Other values (13)
29 

Length

Max length4
Median length4
Mean length3.754386
Min length3

Unique

Unique3 ?
Unique (%)5.3%

Sample

1st row목1동
2nd row목1동
3rd row목2동
4th row목2동
5th row목3동

Common Values

ValueCountFrequency (%)
신정4동 8
14.0%
신월3동 6
10.5%
신월1동 6
10.5%
목4동 5
 
8.8%
신월4동 3
 
5.3%
신월7동 3
 
5.3%
신월5동 3
 
5.3%
목2동 3
 
5.3%
신정3동 3
 
5.3%
신정2동 3
 
5.3%
Other values (8) 14
24.6%

Length

2023-12-12T18:38:20.939386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신정4동 8
14.0%
신월1동 6
10.5%
신월3동 6
10.5%
목4동 5
 
8.8%
목2동 3
 
5.3%
목1동 3
 
5.3%
신정3동 3
 
5.3%
신정2동 3
 
5.3%
신월5동 3
 
5.3%
신월7동 3
 
5.3%
Other values (8) 14
24.6%

소재지 주소
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-12T18:38:21.257504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.649123
Min length17

Characters and Unicode

Total characters1120
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st row서울특별시 양천구 목1동 405-25
2nd row서울특별시 양천구 목1동 405-23
3rd row서울특별시 양천구 목2동 520
4th row서울특별시 양천구 목2동 530-26
5th row서울특별시 양천구 목3동 712-1
ValueCountFrequency (%)
서울특별시 57
25.0%
양천구 57
25.0%
신정4동 8
 
3.5%
신월1동 6
 
2.6%
신월3동 6
 
2.6%
목4동 5
 
2.2%
목2동 3
 
1.3%
신월4동 3
 
1.3%
목1동 3
 
1.3%
신정2동 3
 
1.3%
Other values (67) 77
33.8%
2023-12-12T18:38:21.688602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
171
 
15.3%
1 63
 
5.6%
서 57
 
5.1%
특 57
 
5.1%
별 57
 
5.1%
시 57
 
5.1%
양 57
 
5.1%
천 57
 
5.1%
구 57
 
5.1%
동 57
 
5.1%
Other values (15) 430
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 613
54.7%
Decimal Number 290
25.9%
Space Separator 171
 
15.3%
Dash Punctuation 46
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 57
9.3%
특 57
9.3%
별 57
9.3%
시 57
9.3%
양 57
9.3%
천 57
9.3%
구 57
9.3%
동 57
9.3%
울 57
9.3%
신 43
7.0%
Other values (3) 57
9.3%
Decimal Number
ValueCountFrequency (%)
1 63
21.7%
2 44
15.2%
3 31
10.7%
4 31
10.7%
5 28
9.7%
9 28
9.7%
0 20
 
6.9%
7 17
 
5.9%
6 15
 
5.2%
8 13
 
4.5%
Space Separator
ValueCountFrequency (%)
171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 613
54.7%
Common 507
45.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 57
9.3%
특 57
9.3%
별 57
9.3%
시 57
9.3%
양 57
9.3%
천 57
9.3%
구 57
9.3%
동 57
9.3%
울 57
9.3%
신 43
7.0%
Other values (3) 57
9.3%
Common
ValueCountFrequency (%)
171
33.7%
1 63
 
12.4%
- 46
 
9.1%
2 44
 
8.7%
3 31
 
6.1%
4 31
 
6.1%
5 28
 
5.5%
9 28
 
5.5%
0 20
 
3.9%
7 17
 
3.4%
Other values (2) 28
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 613
54.7%
ASCII 507
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
171
33.7%
1 63
 
12.4%
- 46
 
9.1%
2 44
 
8.7%
3 31
 
6.1%
4 31
 
6.1%
5 28
 
5.5%
9 28
 
5.5%
0 20
 
3.9%
7 17
 
3.4%
Other values (2) 28
 
5.5%
Hangul
ValueCountFrequency (%)
서 57
9.3%
특 57
9.3%
별 57
9.3%
시 57
9.3%
양 57
9.3%
천 57
9.3%
구 57
9.3%
동 57
9.3%
울 57
9.3%
신 43
7.0%
Other values (3) 57
9.3%

배치수량
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
70
57 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row70
2nd row70
3rd row70
4th row70
5th row70

Common Values

ValueCountFrequency (%)
70 57
100.0%

Length

2023-12-12T18:38:21.864268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:38:22.014131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
70 57
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-08-07
57 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-07
2nd row2023-08-07
3rd row2023-08-07
4th row2023-08-07
5th row2023-08-07

Common Values

ValueCountFrequency (%)
2023-08-07 57
100.0%

Length

2023-12-12T18:38:22.140739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:38:22.253078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-07 57
100.0%

Correlations

2023-12-12T18:38:22.316423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분행정동소재지 주소
구분1.0000.0001.000
행정동0.0001.0001.000
소재지 주소1.0001.0001.000
2023-12-12T18:38:22.423214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분행정동
구분1.0000.000
행정동0.0001.000
2023-12-12T18:38:22.543147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분행정동
구분1.0000.000
행정동0.0001.000

Missing values

2023-12-12T18:38:20.464079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:38:20.577482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

구분행정동소재지 주소배치수량데이터기준일자
0전진배치목1동서울특별시 양천구 목1동 405-25702023-08-07
1전진배치목1동서울특별시 양천구 목1동 405-23702023-08-07
2전진배치목2동서울특별시 양천구 목2동 520702023-08-07
3전진배치목2동서울특별시 양천구 목2동 530-26702023-08-07
4전진배치목3동서울특별시 양천구 목3동 712-1702023-08-07
5전진배치목4동서울특별시 양천구 목4동 724-1702023-08-07
6전진배치목4동서울특별시 양천구 목4동 767-1702023-08-07
7전진배치목4동서울특별시 양천구 목4동 772-1702023-08-07
8전진배치목4동서울특별시 양천구 목4동 791-22702023-08-07
9전진배치신월1동서울특별시 양천구 신월1동 240702023-08-07
구분행정동소재지 주소배치수량데이터기준일자
47주민센터신월4동서울특별시 양천구 신월4동 425-2702023-08-07
48주민센터신월5동서울특별시 양천구 신월5동 52-2702023-08-07
49주민센터신월6동서울특별시 양천구 신월6동 559-3702023-08-07
50주민센터신월7동서울특별시 양천구 신월7동 962-10702023-08-07
51주민센터신정1동서울특별시 양천구 신정1동 1051-11702023-08-07
52주민센터신정2동서울특별시 양천구 신정2동 329-9702023-08-07
53주민센터신정3동서울특별시 양천구 신정3동 1263702023-08-07
54주민센터신정4동서울특별시 양천구 신정4동 949-5702023-08-07
55주민센터신정6동서울특별시 양천구 신정6동 322-9702023-08-07
56주민센터신정7동서울특별시 양천구 신정7동 324-5702023-08-07